Though achieving excellent performance in some cases, current unsupervised learning methods for single image denoising usually have constraints in applications. In this paper, we propose a new approach which is more general and applicable to complicated noise models. Utilizing the property of score function, the gradient of logarithmic probability, we define a solving system for denoising. Once the score function of noisy images has been estimated, the denoised result can be obtained through the solving system. Our approach can be applied to multiple noise models, such as the mixture of multiplicative and additive noise combined with structured correlation. Experimental results show that our method is comparable when the noise model is simple, and has good performance in complicated cases where other methods are not applicable or perform poorly.

翻译：尽管当前某些情况下通过无监督学习方法在单一图像降噪方面的表现得到了很大提升，但在应用过程中仍有其限制。本文提出了一种更加通用和适用于复杂噪声模型的新方法。利用评分函数(对数概率的梯度)的性质，定义了用于降噪的求解系统。在估算出带噪声图像的评分函数后，可通过求解系统获得降噪结果。我们的方法可应用于多种噪声模型，如结构关联的乘性和加性噪声混合。实验结果表明，我们的方法在噪声模型简单时可与其他方法相比，而在其他方法不适用或表现不佳的复杂情况下具有良好的性能。